Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use bryjaco/my_tc_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bryjaco/my_tc_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bryjaco/my_tc_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bryjaco/my_tc_model") model = AutoModelForSequenceClassification.from_pretrained("bryjaco/my_tc_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04aa5b27b4cf8f433ad965c7742d6379dd2c4fc5f20f06ad819531c8094fc8b4
- Size of remote file:
- 268 MB
- SHA256:
- c179a72405651ce8d454bb4bbc16481a66c4ace591106d1fc0e8c47e3ca763ab
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